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--- other:python:jyp_steps [2019/11/18 11:04]
jypeter Replaced files links with sharebox links
+++ other:python:jyp_steps [2023/03/29 13:38]
jypeter [Extra numpy information] Corrected links
@@ Line 55: / Line 55: @@
   - always remember that indices start at ''0'' and that the last element of an array is at index ''-1''!\\ First learn about //indexing// and //slicing// by manipulating strings, as shown in [[#part1|Part 1]] above (try '''This document by JY is awesome!'[::-1]'' and '''This document by JY is awesome!'[slice(None, None, -1)]'') 8-)
-  - if you are a Matlab user (but the references are interesting for others as well), you can read the following:
+  - if you are a **Matlab user** (but the references are interesting for others as well), you can read the following:
+    - [[https://www.enthought.com/wp-content/uploads/2019/08/Enthought-MATLAB-to-Python-White-Paper-1.pdf|Migrating from MATLAB to Python]] on the [[https://www.enthought.com/software-development/|Enthought Software Development page]]
     - [[https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html|Numpy for Matlab users]]
     - [[http://mathesaurus.sourceforge.net/matlab-numpy.html|NumPy for MATLAB users]] (nice, but does not seem to be maintained any more)
@@ Line 123: / Line 124: @@
 ==== Extra numpy information ====
-  * More information about array indexing:
+<WRAP center round tip 60%>
+You can also check the [[other:python:misc_by_jyp#numpy_related_stuff|numpy section]] of the //Useful python stuff// page
+</WRAP>
+  * More information about **array indexing**:\\ <wrap em>Always check what you are doing on a simple test case, when you use advanced/fancy indexing!</wrap>
     * Examples:
       * {{ :other:python:indirect_indexing_2.py.txt |}}: Take a vertical slice in a 3D zyx array, along a varying y 'path'
-    * [[https://docs.scipy.org/doc/numpy/user/basics.indexing.html|Indexing]] (//index arrays//, //boolean index arrays//, //np.newaxis//, //Ellipsis//, //variable numbers of indices//, ...)
+    * [[https://numpy.org/doc/stable/user/basics.indexing.html|Array indexing basics (user guide)]] (//index arrays//, //boolean index arrays//, //np.newaxis//, //Ellipsis//, //variable numbers of indices//, ...)
-    * [[https://docs.scipy.org/doc/numpy/user/quickstart.html#fancy-indexing-and-index-tricks|Fancy indexing]] and [[https://docs.scipy.org/doc/numpy/user/quickstart.html#the-ix-function|the ix_() function]]
+    * [[https://numpy.org/doc/stable/reference/arrays.indexing.html|Indexing routines (reference manual)]]
-    * [[https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html|Indexing (in the numpy reference manual)]]
+    * [[https://numpy.org/doc/stable/user/quickstart.html#advanced-indexing-and-index-tricks|Advanced indexing and index tricks]] and [[https://numpy.org/doc/stable/user/quickstart.html#the-ix-function|the ix_() function]]
-    * [[https://docs.scipy.org/doc/numpy/reference/routines.indexing.html#routines-indexing|Indexing routines]]
   * More information about arrays:
-    * [[https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#routines-array-creation|Array creation routines]]
+    * [[https://numpy.org/doc/stable/reference/routines.array-creation.html|Array creation routines]]
-    * [[https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html|Array manipulation routines]]
+    * [[https://numpy.org/doc/stable/reference/routines.array-manipulation.html|Array manipulation routines]]
-    * [[https://docs.scipy.org/doc/numpy/reference/maskedarray.html|Masked arrays]]
+    * [[https://numpy.org/doc/stable/reference/routines.sort.html|Sorting, searching, and counting routines]]
-      * [[https://docs.scipy.org/doc/numpy/reference/routines.ma.html|Masked array operations]]
+    * [[https://numpy.org/doc/stable/reference/maskedarray.html|Masked arrays]]
-  * [[https://docs.scipy.org/doc/numpy/user/misc.html#ieee-754-floating-point-special-values|Dealing with special numerical values]] (//Nan//, //inf//)
+      * [[https://numpy.org/doc/stable/reference/routines.ma.html|Masked array operations]]
-    * If you know that your data has missing values, it is cleaner and safer to handle them with [[https://docs.scipy.org/doc/numpy/reference/maskedarray.html|masked arrays]]!
+  * [[https://numpy.org/doc/stable/user/misc.html#ieee-754-floating-point-special-values|Dealing with special numerical values]] (//Nan//, //inf//)
-    * [[https://docs.scipy.org/doc/numpy/user/misc.html#how-numpy-handles-numerical-exceptions|Handling numerical exceptions]]
+    * If you know that your data has missing values, it is cleaner and safer to handle them with [[https://numpy.org/doc/stable/reference/maskedarray.html|masked arrays]]!
-    * [[https://docs.scipy.org/doc/numpy/reference/routines.err.html|Floating point error handling]]
+    * If you know that some of your data //may// have masked values, play safe by explicitly using ''np.ma.some_function()'' rather than just ''np.some_function()''
+      * More details in the [[https://github.com/numpy/numpy/issues/18675|Why/when does np.something remove the mask of a np.ma array ?]] discussion
+    * [[https://numpy.org/doc/stable/user/misc.html#how-numpy-handles-numerical-exceptions|Handling numerical exceptions]]
+    * [[https://numpy.org/doc/stable/reference/routines.err.html|Floating point error handling]]
-===== cdms2 and netCDF4 =====
+===== Using NetCDF files with Python =====
-There is a good chance that your input array data will come from a file in the [[other:newppl:starting#netcdf_and_file_formats|NetCDF format]].
+<note tip>People using CMIPn and model data on the IPSL servers can easily search and process NetCDF files using:
+  * the [[https://climaf.readthedocs.io/|Climate Model Assessment Framework (CliMAF)]] environment
+  * and the [[https://github.com/jservonnat/C-ESM-EP/wiki|CliMAF Earth System Evaluation Platform (C-ESM-EP)]]
+</note>
-Depending on which [[other:python:starting#some_python_distributions|python distribution]] you are using, you can use the //cdms2// or or //netCDF4// modules to read the data.
+  * There is a good chance that your input array data will be stored in a  [[other:newppl:starting#netcdf_and_related_conventions|NetCDF]] file.
+  * There may be different ways of dealing with NetCDF files, depending on which [[other:python:starting#some_python_distributions|python distribution]] you have access to
 ==== cdms2 ====
-Summary: cdms2 can read/write netCDF files (and read //grads// dat+ctl files) and provides a higher level interface than netCDF4. cdms2 is available in the [[other:python:starting#uv-cdat|UV-CDAT distribution]], and can theoretically be installed independently of UV-CDAT (e.g. it will be installed when you install [[https://cmor.llnl.gov/mydoc_cmor3_conda/|CMOR in conda)]]. When you can use cdms2, you also have access to //cdtime//, that is very useful for handling time axis data.
+Summary: cdms2 can read/write netCDF files (and read //grads// dat+ctl files) and provides a higher level interface than netCDF4. cdms2 is available in the [[other:python:starting#cdat|CDAT distribution]], and can theoretically be installed independently of CDAT (e.g. it will be installed when you install [[https://cmor.llnl.gov/mydoc_cmor3_conda/|CMOR in conda)]]. When you can use cdms2, you also have access to //cdtime//, that is very useful for handling time axis data.
 How to get started:
@@ Line 155: / Line 168: @@
     - you have to replace //cdms// with **cdms2**, and //MV// with **MV2** (sooorry about that, the tutorial was written when CDAT was based on //Numeric// instead of //numpy// to handle array data)
   - read the [[http://cdms.readthedocs.io/en/docstanya/index.html|official cdms documentation]] (link may change)
+==== xarray ====
+Summary: [[http://xarray.pydata.org/en/stable/|xarray]] is an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun! [...] It is particularly tailored to working with netCDF files
+=== Some xarray related resources ===
+Note: more packages (than listed below) may be listed in the [[other:uvcdat:cdat_conda:cdat_8_2_1#extra_packages_list|Extra packages list]]
+  * [[https://xcdat.readthedocs.io/|xcdat]]: xarray extended with Climate Data Analysis Tools
+  * [[https://xoa.readthedocs.io/en/latest/|xoa]]: xarray-based ocean analysis library
+  * [[https://uxarray.readthedocs.io/|uxarray]]: provide xarray styled functionality for unstructured grid datasets following [[https://ugrid-conventions.github.io/ugrid-conventions/|UGRID Conventions]]
@@ Line 165: / Line 193: @@
 ===== CDAT-related resources =====
-Some links, in case they can't be found easily on the [[https://uv-cdat.llnl.gov|UV-CDAT]] web site...
+Some links, in case they can't be found easily on the [[https://cdat.llnl.gov|CDAT]] web site...
-  * [[https://uv-cdat.llnl.gov/tutorials.html|Tutorials in ipython notebooks]]
+  * [[https://cdat.llnl.gov/tutorials.html|Tutorials in ipython notebooks]]
   * [[http://cdat-vcs.readthedocs.io/en/latest/|VCS: Visualization Control System]]
     * [[https://github.com/CDAT/vcs/issues/238|Colormaps in vcs examples]]
@@ Line 175: / Line 203: @@
 <note important>
-The full content of this //matplotlib// section has been moved to\\ [[other:python:matplotlib_by_jyp|Working with matplotlib (JYP version)]]\\ after becoming too big to manage here</note>
+The full content of this //matplotlib// section has been moved to\\ [[other:python:matplotlib_by_jyp|Working with matplotlib (JYP version)]]\\ after becoming too big to manage here
+\\ Note: [[other:python:maps_by_jyp|Plotting maps with matplotlib+cartopy]] (examples provided by JYP)
+</note>
 Summary: there are lots of python libraries that you can use for plotting, but Matplotlib has become a //de facto// standard
@@ Line 194: / Line 225: @@
     * [[https://uxknowledgebase.com/tables-other-charts-data-visualization-part-2-cfc582e4712c|Tables & Other Charts — Data Visualization Part 2]]
     * [[https://uxknowledgebase.com/tables-other-charts-data-visualization-part-3-5bfab15ce525|Tables & Other Charts — Data Visualization Part 3]]
-  * Working with colors
+  * **IPCC**-related //stuff//...
+    * [[https://www.ipcc.ch/site/assets/uploads/2019/04/IPCC-visual-style-guide.pdf|IPCC Visual Style Guide for Authors]]
+    * [[https://wg1.ipcc.ch/sites/default/files/documents/ipcc_visual-identity_guidelines.pdf|A new assessment cycle,A new visual identity]]
+    * [[https://link.springer.com/article/10.1007/s10584-019-02537-z|Communication of IPCC visuals: IPCC authors’ views and assessments of visual complexity]]
+    * [[https://www.carbonbrief.org/guest-post-the-perils-of-counter-intuitive-design-in-ipcc-graphics|The perils of counter-intuitive design in IPCC graphics]]
+  * Working with **colors**
+    * Choosing specific colors: use [[https://www.w3schools.com/colors/colors_names.asp|HTML color names]], the [[https://www.w3schools.com/colors/colors_picker.asp|HTML color picker]], etc...
+    * **Do not use the outdated //rainbow// and //jet// colormaps!**
+      * [[https://pjbartlein.github.io/datagraphics/index.html|The End of the Rainbow?  Color Schemes for Improved Data Graphics]] (Light and Bartlein, EOS 2004, including replies and comments)
+      * [[http://colorspace.r-forge.r-project.org/articles/endrainbow.html|Somewhere over the Rainbow]]
+      * [[https://www.nature.com/articles/s41467-020-19160-7|The misuse of colour in science communication]]
     * [[https://matplotlib.org/users/colormaps.html|Choosing colormaps]]
     * [[https://matplotlib.org/cmocean/|cmocean: Beautiful colormaps for oceanography]]
     * [[https://jiffyclub.github.io/palettable/|Palettable: Color palettes for Python]]
     * [[http://colorbrewer2.org|ColorBrewer 2.0]] is a tool that can help you understand, and experiment with //sequential//, //diverging// and //qualitative// colormaps
+    * The [[http://hclwizard.org/|hclwizard]] provides tools for manipulating and assessing colors and palettes based on the underlying ''colorspace'' software
+    * NCL (NCAR Command Language) [[https://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml|Color table Gallery]]
+    * JYP's favorite title: [[https://www.researchgate.net/publication/220943662_The_Which_Blair_Project_A_Quick_Visual_Method_for_Evaluating_Perceptual_Color_Maps|The "Which Blair Project": A Quick Visual Method for Evaluating Perceptual Color Maps]]
 ===== Basemap =====
-<note warning>Basemap is going to be slowly phased out, in favor of [[#cartopy]]\\ More information in this:
+<note warning>Basemap is going to be slowly phased out, in favor of [[#cartopy_iris|cartopy]]\\ More information in this:
   * [[https://github.com/SciTools/cartopy/issues/920|cartopy github issue]]
   * [[https://github.com/matplotlib/basemap/issues/267|basemap github issue]]
@@ Line 223: / Line 267: @@
 ===== Cartopy + Iris =====
-Summary: //Cartopy is a Python package for advanced map generation with a simple matplotlib interface// and //Iris is a Python package for analysing and visualising meteorological and oceanographic data sets//
+Summary:
+  * **Cartopy** is //a matplolib-based Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses//
+  * **Iris** is //a powerful, format-agnostic, community-driven Python package for analysing and visualising Earth science data.//
-Where: [[http://scitools.org.uk/cartopy/docs/latest/|Cartopy]] and [[http://scitools.org.uk/iris/index.html|Iris]] web sites
+Where: [[http://scitools.org.uk/cartopy/docs/latest/|Cartopy]] and [[https://scitools-iris.readthedocs.io/en/stable/|Iris]] web sites
 Examples:
-  * [[http://scitools.org.uk/cartopy/docs/latest/gallery.html|Gallery on the Cartopy web site]]
+  * [[other:python:maps_by_jyp|Examples provided by JYP]]
-  * [[http://scitools.org.uk/iris/docs/latest/gallery.html|Gallery on the Iris web site]]
+  * Official gallery pages: [[https://scitools.org.uk/cartopy/docs/latest/gallery/index.html|Cartopy]] - [[https://scitools-iris.readthedocs.io/en/stable/generated/gallery/|Iris]]
-  * [[http://scitools.org.uk/iris/docs/latest/examples/index.html|Examples on the Iris web site]]
-Help on //stack overflow//: [[https://stackoverflow.com/questions/tagged/cartopy|cartopy help]]
+Help on //stack overflow//: [[https://stackoverflow.com/questions/tagged/cartopy|Cartopy help]] - [[https://stackoverflow.com/questions/tagged/python-iris|Iris help]]
 ===== Maps and projections resources =====
@@ Line 258: / Line 303: @@
 We list here some resources about non-NetCDF data formats that can be useful
+==== The shelve package ====
+The [[https://docs.python.org/3/library/shelve.html|built-in shelve package]], can be easily used for storing data (python objects like lists, dictionaries, numpy arrays that are not too big, ...) on disk and retrieving them later
+Use case:
+  - Use a script do to the heavy data pre-processing and store the (intermediate) results in a file using ''shelve'', or update the results
+  - Use another script for plotting the results stored with ''shelve''. This way you don't have to wait for the pre-processing step to finish each time you want to improve your plot(s)
+Warning:
+  * read the [[https://docs.python.org/3/library/shelve.html|documentation]] and the example carefully (it's quite small)
+    * if you get the impression that the data is not saved correctly, re-read the parts about updating correctly the content of the shelve file
+    * you should be able to store most python objects in a shelve file, but it is safer to make tests
+  * do not forget to close the output file
+  * if you are dealing with big arrays and want to avoid performance issues, you should use netCDF files for storing the intermediate results
 ==== json files ====
@@ Line 264: / Line 323: @@
 //json// files look basically like a **list of (nested) python dictionaries** that would have been dumped to a text file
-  * [[https://docs.python.org/2/library/json.html|json module]] documentation
+  * [[https://docs.python.org/3/library/json.html|json module]] documentation
   * [[https://realpython.com/python-json/|Working With JSON Data in Python]] tutorial
   * example script: ''/home/users/jypeter/CDAT/Progs/Devel/beaugendre/nc2json.py''
@@ Line 306: / Line 365: @@
 Where: [[http://www.scipy-lectures.org/_downloads/ScipyLectures-simple.pdf|pdf]] - [[http://www.scipy-lectures.org/|html]]
-This is **a really nice and useful document** that is regularly updated and used for the [[https://www.euroscipy.org/|EuroScipy]] tutorials. You will learn more things about python, numpy and matplotlib, debugging and optimizing scripts, and also learn about using python for statistics, image processing, machine learning, washing dishes (this is just to check if you have read this page), etc...
+This is **a really nice and useful document** that is regularly updated and used for the [[https://www.euroscipy.org/|EuroScipy]] tutorials.
+This document will teach you even more things about python, numpy and matplotlib, debugging and optimizing scripts, and about using python for statistics, image processing, machine learning, washing dishes (this is just to check if you have read this page), etc...
+  * Example: the [[http://www.scipy-lectures.org/packages/statistics/index.html|Statistics in Python]] tutorial that combines [[other:python:jyp_steps#pandas|Pandas]], [[http://statsmodels.sourceforge.net/|Statsmodels]] and [[http://seaborn.pydata.org/|Seaborn]]
+===== statsmodels =====
+[[https://www.statsmodels.org/|statsmodels ]] is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration.
+===== scikit-learn =====
+[[http://scikit-learn.org/|scikit-learn]] is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities.
+===== scikit-image =====
+[[https://scikit-image.org/|scikit-image]] is a collection of algorithms for image processing in Python
 ===== Quick Reference and cheat sheets =====
@@ Line 316: / Line 390: @@
   * [[https://www.cheatography.com/weidadeyue/cheat-sheets/jupyter-notebook/pdf_bw/|Jupyter Notebook Keyboard Shortcuts]]
+===== Miscellaneous Python stuff =====
+Check the page about [[other:python:misc_by_jyp|useful python stuff that has not been sorted yet]]
 ===== Misc tutorials =====
@@ Line 347: / Line 425: @@
 Depending on the distribution, the editor and the programming environment you use, you may have access to a graphical version of the debugger. UV-CDAT users can use ''pydebug my_script.py''
+===== jupyter and notebook stuff =====
+FIXME Misc notes, resources and links to organize later
+  * [[https://beta.jupyterbook.org/|jupyter {book}]]: Jupyter Book is an open source project for building beautiful, publication-quality books and documents from computational material.
 ===== Using a Python IDE =====
@@ Line 389: / Line 473: @@
 ===== Python 2.7 vs Python 3 =====
-The official [[https://docs.python.org/2.7/howto/pyporting.html|Porting Python 2 Code to Python 3]] page gives the required information to make the transition from python 2 to python 3. It is still safe to use Python 2.7, so there is no rush to change to Python 3.
+It is still safe to use Python 2.7, but **you should consider upgrading to Python 3**, unless some key modules you need are not compatible (yet) with Python 3
+You should start writing code that will, when possible, work both in Python 2 and Python 3
+Some interesting reading:
+  * [[https://docs.python.org/3/whatsnew/3.0.html|What’s New In Python 3.0]].\\ Examples:
+    * ''print'' is now a function. Use ''print('Hello')''
+    * You cannot test a difference with ''<>'' any longer! Use ''!=''
+  * The official [[https://docs.python.org/2.7/howto/pyporting.html|Porting Python 2 Code to Python 3]] page gives the required information to make the transition from python 2 to python 3.
 ===== What now? =====

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